实用机器学习

Practical Machine Learning

Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.

约翰霍普金斯大学

Coursera

计算机

简单(初级)

14 小时

Sponsored\Ad:本课程链接由Coursera和Linkshare共同提供
  • 英语
  • 620

课程概况

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

你将学到什么

Describe machine learning methods such as regression or classification trees

Explain the complete process of building prediction functions

Understand concepts such as training and tests sets, overfitting, and error rates

Use the basic components of building and applying prediction functions

课程大纲

周1
完成时间为 2 小时
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
9 个视频 (总计 73 分钟), 3 个阅读材料, 1 个测验

周2
完成时间为 2 小时
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
9 个视频 (总计 96 分钟), 1 个测验

周3
完成时间为 1 小时
Week 3: Predicting with trees, Random Forests, & Model Based Predictions
This week we introduce a number of machine learning algorithms you can use to complete your course project.
5 个视频 (总计 48 分钟), 1 个测验

周4
完成时间为 4 小时
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.

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